Code Tutorial#
import torch
import IPython.display as ipd
sr = 44100
duration = 5
audio_sample = torch.randn(1, sr * duration)
ipd.Audio(audio_sample.numpy(), rate=sr)
Stable Audio Open Tutorial#
Stable Audio Open is fully avaiable through HuggingFace. To run Stable Audio Open locally, you’ll first need to generate a $HF_TOKEN for yourself, which can be done here https://huggingface.co/docs/huggingface_hub/en/quick-start#authentication (which you will first need a HuggingFace account for). Once you generate the token, you should export it as an environment variable with a bash command like
export HF_TOKEN="YOUR_HF_TOKEN"
The rest of the tutorial very much follows the demo design of the public Stable Audio Open resources:
First, we’ll install some dependencies if you don’t already have them. Stable-Audio-Tools can be a bit finnicky to install directly, so we suggest making a dedicated virtual envinroment (and not conda) to run this notebook.
!pip install torch torchaudio torchvision stable-audio-tools einops
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Using cached argparse-1.4.0-py2.py3-none-any.whl (23 kB)
Installing collected packages: argparse
Successfully installed argparse-1.4.0
If running this locally, you can simply set the HF_TOKEN in your local environment (as done below). If you’re using a collab notebook, you first need to upload your HF_TOKEN as a “secret key” to your collab, and the below command won’t have any affect in that case.
import os
os.environ['HF_TOKEN'] = 'Your API key'
Next, we can load the model from huggingface. Note that there are some known dependency issues with stable-audio-tools on M1 Macs, so we recommend running this as a collab notebook (or on some linux system)
import torch
import torchaudio
# import librosa
from einops import rearrange
from stable_audio_tools import get_pretrained_model
from stable_audio_tools.inference.generation import generate_diffusion_cond
import IPython.display as ipd
from functools import partial
device = "cuda" if torch.cuda.is_available() else "cpu"
# Download model
model, model_config = get_pretrained_model("stabilityai/stable-audio-open-1.0")
sample_rate = model_config["sample_rate"]
sample_size = model_config["sample_size"]
model = model.to(device)
/Users/seungheond/anaconda3/envs/p310/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
---------------------------------------------------------------------------
HTTPError Traceback (most recent call last)
File ~/anaconda3/envs/p310/lib/python3.10/site-packages/huggingface_hub/utils/_http.py:406, in hf_raise_for_status(response, endpoint_name)
405 try:
--> 406 response.raise_for_status()
407 except HTTPError as e:
File ~/anaconda3/envs/p310/lib/python3.10/site-packages/requests/models.py:1024, in Response.raise_for_status(self)
1023 if http_error_msg:
-> 1024 raise HTTPError(http_error_msg, response=self)
HTTPError: 401 Client Error: Unauthorized for url: https://huggingface.co/stabilityai/stable-audio-open-1.0/resolve/main/model_config.json
The above exception was the direct cause of the following exception:
GatedRepoError Traceback (most recent call last)
Cell In[4], line 13
10 device = "cuda" if torch.cuda.is_available() else "cpu"
12 # Download model
---> 13 model, model_config = get_pretrained_model("stabilityai/stable-audio-open-1.0")
14 sample_rate = model_config["sample_rate"]
15 sample_size = model_config["sample_size"]
File ~/anaconda3/envs/p310/lib/python3.10/site-packages/stable_audio_tools/models/pretrained.py:10, in get_pretrained_model(name)
8 def get_pretrained_model(name: str):
---> 10 model_config_path = hf_hub_download(name, filename="model_config.json", repo_type='model')
12 with open(model_config_path) as f:
13 model_config = json.load(f)
File ~/anaconda3/envs/p310/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:114, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs)
111 if check_use_auth_token:
112 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)
--> 114 return fn(*args, **kwargs)
File ~/anaconda3/envs/p310/lib/python3.10/site-packages/huggingface_hub/file_download.py:862, in hf_hub_download(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, user_agent, force_download, proxies, etag_timeout, token, local_files_only, headers, endpoint, resume_download, force_filename, local_dir_use_symlinks)
842 return _hf_hub_download_to_local_dir(
843 # Destination
844 local_dir=local_dir,
(...)
859 local_files_only=local_files_only,
860 )
861 else:
--> 862 return _hf_hub_download_to_cache_dir(
863 # Destination
864 cache_dir=cache_dir,
865 # File info
866 repo_id=repo_id,
867 filename=filename,
868 repo_type=repo_type,
869 revision=revision,
870 # HTTP info
871 endpoint=endpoint,
872 etag_timeout=etag_timeout,
873 headers=headers,
874 proxies=proxies,
875 token=token,
876 # Additional options
877 local_files_only=local_files_only,
878 force_download=force_download,
879 )
File ~/anaconda3/envs/p310/lib/python3.10/site-packages/huggingface_hub/file_download.py:969, in _hf_hub_download_to_cache_dir(cache_dir, repo_id, filename, repo_type, revision, endpoint, etag_timeout, headers, proxies, token, local_files_only, force_download)
966 return pointer_path
968 # Otherwise, raise appropriate error
--> 969 _raise_on_head_call_error(head_call_error, force_download, local_files_only)
971 # From now on, etag, commit_hash, url and size are not None.
972 assert etag is not None, "etag must have been retrieved from server"
File ~/anaconda3/envs/p310/lib/python3.10/site-packages/huggingface_hub/file_download.py:1484, in _raise_on_head_call_error(head_call_error, force_download, local_files_only)
1478 raise LocalEntryNotFoundError(
1479 "Cannot find the requested files in the disk cache and outgoing traffic has been disabled. To enable"
1480 " hf.co look-ups and downloads online, set 'local_files_only' to False."
1481 )
1482 elif isinstance(head_call_error, RepositoryNotFoundError) or isinstance(head_call_error, GatedRepoError):
1483 # Repo not found or gated => let's raise the actual error
-> 1484 raise head_call_error
1485 else:
1486 # Otherwise: most likely a connection issue or Hub downtime => let's warn the user
1487 raise LocalEntryNotFoundError(
1488 "An error happened while trying to locate the file on the Hub and we cannot find the requested files"
1489 " in the local cache. Please check your connection and try again or make sure your Internet connection"
1490 " is on."
1491 ) from head_call_error
File ~/anaconda3/envs/p310/lib/python3.10/site-packages/huggingface_hub/file_download.py:1376, in _get_metadata_or_catch_error(repo_id, filename, repo_type, revision, endpoint, proxies, etag_timeout, headers, token, local_files_only, relative_filename, storage_folder)
1374 try:
1375 try:
-> 1376 metadata = get_hf_file_metadata(
1377 url=url, proxies=proxies, timeout=etag_timeout, headers=headers, token=token
1378 )
1379 except EntryNotFoundError as http_error:
1380 if storage_folder is not None and relative_filename is not None:
1381 # Cache the non-existence of the file
File ~/anaconda3/envs/p310/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:114, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs)
111 if check_use_auth_token:
112 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)
--> 114 return fn(*args, **kwargs)
File ~/anaconda3/envs/p310/lib/python3.10/site-packages/huggingface_hub/file_download.py:1296, in get_hf_file_metadata(url, token, proxies, timeout, library_name, library_version, user_agent, headers)
1293 headers["Accept-Encoding"] = "identity" # prevent any compression => we want to know the real size of the file
1295 # Retrieve metadata
-> 1296 r = _request_wrapper(
1297 method="HEAD",
1298 url=url,
1299 headers=headers,
1300 allow_redirects=False,
1301 follow_relative_redirects=True,
1302 proxies=proxies,
1303 timeout=timeout,
1304 )
1305 hf_raise_for_status(r)
1307 # Return
File ~/anaconda3/envs/p310/lib/python3.10/site-packages/huggingface_hub/file_download.py:277, in _request_wrapper(method, url, follow_relative_redirects, **params)
275 # Recursively follow relative redirects
276 if follow_relative_redirects:
--> 277 response = _request_wrapper(
278 method=method,
279 url=url,
280 follow_relative_redirects=False,
281 **params,
282 )
284 # If redirection, we redirect only relative paths.
285 # This is useful in case of a renamed repository.
286 if 300 <= response.status_code <= 399:
File ~/anaconda3/envs/p310/lib/python3.10/site-packages/huggingface_hub/file_download.py:301, in _request_wrapper(method, url, follow_relative_redirects, **params)
299 # Perform request and return if status_code is not in the retry list.
300 response = get_session().request(method=method, url=url, **params)
--> 301 hf_raise_for_status(response)
302 return response
File ~/anaconda3/envs/p310/lib/python3.10/site-packages/huggingface_hub/utils/_http.py:423, in hf_raise_for_status(response, endpoint_name)
419 elif error_code == "GatedRepo":
420 message = (
421 f"{response.status_code} Client Error." + "\n\n" + f"Cannot access gated repo for url {response.url}."
422 )
--> 423 raise _format(GatedRepoError, message, response) from e
425 elif error_message == "Access to this resource is disabled.":
426 message = (
427 f"{response.status_code} Client Error."
428 + "\n\n"
(...)
431 + "Access to this resource is disabled."
432 )
GatedRepoError: 401 Client Error. (Request ID: Root=1-6730f40e-0035d0002276c8da3c048b22;8b4d2fe6-7b98-4702-a05b-cd7565572fb1)
Cannot access gated repo for url https://huggingface.co/stabilityai/stable-audio-open-1.0/resolve/main/model_config.json.
Access to model stabilityai/stable-audio-open-1.0 is restricted. You must have access to it and be authenticated to access it. Please log in.
First we’ll wrap the sampling code in a simpler wrapper, as there’s a few parameters that need to be provided but are not strictly useful to play around with.
# this just cleans things up a bit so the code below highlights the important knobs
easy_generate = partial(generate_diffusion_cond, sample_size=sample_size, sigma_min=0.3, sigma_max=500, device=device)
Next we can define our conditioning, which for the default Stable Audio Open involves text, timing, and overall length.
# Set up text and timing conditioning
conditioning = [{
"prompt": "clean guitar, sweep picking, 140 bpm, G minor",
"seconds_start": 0, # this says "where" in time the sample is in the song,
"seconds_total": 30 # total sample length in seconds, rest gets padded with silency
}]
seed = 1000
n_steps = 50
cfg = 7.5
sampler = "dpmpp-3m-sde"
output = easy_generate(
model,
conditioning=conditioning,
steps=n_steps, # number of diffusion steps to run
cfg_scale=cfg, # classifier free guidance guidance scale
sampler_type=sampler, # sampling "algorithm", check out https://github.com/Stability-AI/stable-audio-tools/blob/main/stable_audio_tools/inference/sampling.py#L177 for more options
seed=seed,
)
# Rearrange audio batch to a single sequence
output = rearrange(output, "b d n -> d (b n)")
# Peak normalize, clip, convert to int16, and save to file
output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()[:, :round(conditioning[0]['seconds_total']*sample_rate)]
Now we can listen to the output! Note: if running on a collab notebook, rendering audio will stop the autosave feature, so be sure to delete the block outputs if you want to turn this back on!
ipd.display(ipd.Audio(output, rate=sample_rate))